Dimensional Stacking for Machine Learning in ToF‐SIMS Analysis of Heterostructures

Abbasi, Kévin; Smith, Hugh; Hoffman, Matthew D.; Farghadany, Elahe; Bruckman, Laura S.; Sehirlioglu, Alp

doi:10.1002/admi.202001648

Cited by 5 publications

(4 citation statements)

References 98 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We note that the approach recommended with respect to data curation, leveraging dimensional stacking in systematic global scaling to identify contributors, followed by local scaling to mask or enhance features, is valid well beyond switching RPFM analysis: this approach can be extended not only to any other spectroscopic RPFM techniques, but to any multidimensional SPM characterization (e.g., electrochemical strain microscopy, ESM) or multidimensional data set created by use of different characterization methods as shown previously. [31,50]…”

Section: Resultsmentioning

confidence: 99%

Mask or Enhance: Data Curation Aiding the Discovery of Piezoresponse Force Microscopy Contributors

Ligonde

Williams

Gaponenko

et al. 2023

Advanced Physics Research

View full text Add to dashboard Cite

Piezoresponse force microscopy (PFM) is routinely used to probe the nanoscale electromechanical response of ferroelectric and piezoelectric materials. However, many challenges remain in the interpretation of the recovered signal. Specifically, many non‐ferroelectric contributions affect the measured response, ranging from electrostatics, to charge injection and trapping, and topographic cross‐talk. Recently, machine learning (ML) has been utilized to identify multiple contributors within complex data systems, such as PFM response. A substantial advancement in ML approaches for PFM techniques is offered by dimensional stacking, enabling encoding of physical and/or chemical correlations within the materials' response across different data dimensions spanning varying ranges. However, dimensional stacking requires appropriate scaling for each dimension (before ML analysis) to minimize undesired information loss. Here, the impact of clustering globally and locally scaled parameters in polarization switching experiments via resonant PFM (RPFM) are discussed. Specifically, dimensional stacking of scaled parameters can mask or enhance ferroelectric and non‐ferroelectric behaviors, and aid identification of various physical phenomena contributing to the measured RPFM response. This study highlights the importance of data curation for ML, and its role in identifying signal contributors to scanning probe microscopy (SPM)‐based techniques with multidimensional data, such as resonant and/or spectroscopic SPM.

show abstract

Section: Resultsmentioning

confidence: 99%

Mask or Enhance: Data Curation Aiding the Discovery of Piezoresponse Force Microscopy Contributors

Ligonde

Williams

Gaponenko

et al. 2023

Advanced Physics Research

View full text Add to dashboard Cite

show abstract

“…A poling and relaxation behavior was identified, and the evolution of those behaviors was tracked alongside the phase diagram of the materials. This reductive strategy was applied to ToF-SIMS by Abbassi et al, with four LaAlO 3 and SrAlO 3 heterostructures studied through PCA and NMF [197]. It was shown that the strategy provided dimensional stacking statistics while maintaining the separability of the different specimens.…”

Section: To Address Sims Data Challengementioning

confidence: 99%

Secondary Ion Mass Spectral Imaging of Metals and Alloys

Shen,

Howard,

2024

Materials

View full text Add to dashboard Cite

Secondary Ion Mass Spectrometry (SIMS) is an outstanding technique for Mass Spectral Imaging (MSI) due to its notable advantages, including high sensitivity, selectivity, and high dynamic range. As a result, SIMS has been employed across many domains of science. In this review, we provide an in-depth overview of the fundamental principles underlying SIMS, followed by an account of the recent development of SIMS instruments. The review encompasses various applications of specific SIMS instruments, notably static SIMS with time-of-flight SIMS (ToF-SIMS) as a widely used platform and dynamic SIMS with Nano SIMS and large geometry SIMS as successful instruments. We particularly focus on SIMS utility in microanalysis and imaging of metals and alloys as materials of interest. Additionally, we discuss the challenges in big SIMS data analysis and give examples of machine leaning (ML) and Artificial Intelligence (AI) for effective MSI data analysis. Finally, we recommend the outlook of SIMS development. It is anticipated that in situ and operando SIMS has the potential to significantly enhance the investigation of metals and alloys by enabling real-time examinations of material surfaces and interfaces during dynamic transformations.

show abstract

“…PCA and NMF techniques are often employed with ToF-SIMS analysis in the surface analysis and one-dimensional spectral analysis of sample layers [ 66 , 120 ]. Recently, Gardner et al and Madiona et al used the ML artificial neural network technique of SOMs to interpret complex ToF-SIMS data [ 116 , 119 ].…”

Section: Machine Learning For Tof-sims and Maldi Data Analysismentioning

confidence: 99%

Mass Spectral Imaging to Map Plant–Microbe Interactions

Parker

Hanley

2023

Microorganisms

View full text Add to dashboard Cite

Plant–microbe interactions are of rising interest in plant sustainability, biomass production, plant biology, and systems biology. These interactions have been a challenge to detect until recent advancements in mass spectrometry imaging. Plants and microbes interact in four main regions within the plant, the rhizosphere, endosphere, phyllosphere, and spermosphere. This mini review covers the challenges within investigations of plant and microbe interactions. We highlight the importance of sample preparation and comparisons among time-of-flight secondary ion mass spectroscopy (ToF-SIMS), matrix-assisted laser desorption/ionization (MALDI), laser desorption ionization (LDI/LDPI), and desorption electrospray ionization (DESI) techniques used for the analysis of these interactions. Using mass spectral imaging (MSI) to study plants and microbes offers advantages in understanding microbe and host interactions at the molecular level with single-cell and community communication information. More research utilizing MSI has emerged in the past several years. We first introduce the principles of major MSI techniques that have been employed in the research of microorganisms. An overview of proper sample preparation methods is offered as a prerequisite for successful MSI analysis. Traditionally, dried or cryogenically prepared, frozen samples have been used; however, they do not provide a true representation of the bacterial biofilms compared to living cell analysis and chemical imaging. New developments such as microfluidic devices that can be used under a vacuum are highly desirable for the application of MSI techniques, such as ToF-SIMS, because they have a subcellular spatial resolution to map and image plant and microbe interactions, including the potential to elucidate metabolic pathways and cell-to-cell interactions. Promising results due to recent MSI advancements in the past five years are selected and highlighted. The latest developments utilizing machine learning are captured as an important outlook for maximal output using MSI to study microorganisms.

show abstract

Dimensional Stacking for Machine Learning in ToF‐SIMS Analysis of Heterostructures

Cited by 5 publications

References 98 publications

Mask or Enhance: Data Curation Aiding the Discovery of Piezoresponse Force Microscopy Contributors

Mask or Enhance: Data Curation Aiding the Discovery of Piezoresponse Force Microscopy Contributors

Secondary Ion Mass Spectral Imaging of Metals and Alloys

Mass Spectral Imaging to Map Plant–Microbe Interactions

Contact Info

Product

Resources

About